Clustering and Averaging of Images in Single-Particle Analysis

抄録

Single particle analysis is a straightforward method for studying the structures of macromoleculest hat cannot be crystallized. It builds three-dimensionasl tructures of particles by estimating the projectiona ngleso f their randomlyo riented electron-microscopiicm ages. The existing methods divide the images into clusters, build class averages for the clusters, and estimate the projection angle of each cluster. However, the clustering and the averaged images are highly sensitive to the choice of reference images and mask patterns for each cluster. Thus, the analyses are neither robust nor automatic, and their results depend heavily on the intuition and experience of researchers who set references.
We have been developing a software system for single-particle analysis with new clustering and averaginga lgorithms for building the three-dimensionasl tructures of target molecules. In this paper, we focus on the algorithmsf or the robust image-processinogf the electronm icroscopic images in our system.